Estimating the rate of cell type degeneration from epigenetic sequencing of cell-free DNA

Abstract

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death. Here, we develop a method to accurately estimate the relative abundances of cell types contributing to cfDNA. We leverage the distinct DNA methylation profile of each cell type throughout the body. Decomposing the cfDNA mixture is difficult, as fragments from relevant cell types may only be present in a small amount. We propose an algorithm, CelFiE, that estimates cell type proportion from both whole genome cfDNA input and reference data. CelFiE accommodates low coverage data, does not rely on CpG site curation, and estimates contributions from multiple unknown cell types that are not available in reference data. In simulations we show that CelFiE can accurately estimate known and unknown cell type of origin of cfDNA mixtures in low coverage and noisy data. Simulations also demonstrate that we can effectively estimate cfDNA originating from rare cell types composing less than 0.01% of the total cfDNA. To validate CelFiE, we use a positive control: cfDNA extracted from pregnant and non-pregnant women. CelFiE estimates a large placenta component specifically in pregnant women (p = 9.1 × 10 −5 ). Finally, we use CelFiE to decompose cfDNA from ALS patients and age matched controls. We find increased cfDNA concentrations in ALS patients (p = 3.0 × 10 −3 ). Specifically, CelFiE estimates increased skeletal muscle component in the cfDNA of ALS patients (p = 2.6 × 10 −3 ), which is consistent with muscle impairment characterizing ALS. Quantification of skeletal muscle death in ALS is novel, and overall suggests that CelFiE may be a useful tool for biomarker discovery and monitoring of disease progression.

Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

“…The estimated cell-type contributions in our samples, obtained from healthy volunteers, are similar to what has previously been described by Moss [3] and Caggiano [25], with the majority of cfDNA is originating from leukocytes. Furthermore, a smaller but substantial fraction of the cfDNA is derived from erythrocyte progenitors in these two studies, and our results further support these findings.…”

Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

“…The estimated cell-type contributions in our samples, obtained from healthy volunteers, are similar to what has previously been described by Moss 3 and Caggiano 25 , with the majority of cfDNA is originating from leukocytes. Furthermore, a smaller but substantial fraction of the cfDNA is derived from erythrocyte progenitors in these two studies, and our results further support these findings.…”

“…In order to confirm these results, we used an independent reference dataset and deconvolution method (CelFiE) 25 . We observed similar changes in NK cell contribution (figure 3G & 3H), with a mean absolute change of 13.81% in EDTA tubes (median at T0 = 3.12%, median at T72 = 31.97%).…”

“…Deconvolution of plasma cfDNA samples was done with CelFiE (commit d3f975f) and its accompanying reference dataset 25 . In order to adapt the WGBS dataset (sourced from ENCODE 26 and BLUEPRINT 27 ) for cf-RRBS data, we removed all CpGs not covered in cf-RRBS from the WGBS dataset (based on the Mspl regions between 20-200 bp generated with mkrrgenome).…”

“…In order to adapt the WGBS dataset (sourced from ENCODE 26 and BLUEPRINT 27 ) for cf-RRBS data, we removed all CpGs not covered in cf-RRBS from the WGBS dataset (based on the Mspl regions between 20-200 bp generated with mkrrgenome). Afterwards, tissue informative markers were generated as described by Caggiano and colleagues 25 and deconvolution was performed with CelFiE (https://github.com/christacaggiano/celfie), directly from coverage files obtained from Bismark.…”